Nature mental health最新文献

{"title":"The harm of withholding humanitarian aid to health and wellbeing","authors":"Maggie Zraly","doi":"10.1038/s44220-025-00426-2","DOIUrl":"10.1038/s44220-025-00426-2","url":null,"abstract":"The Trump Administration’s executive orders that target diversity, equity and inclusion initiatives, humanitarian approaches to migration management, and climate justice policies threaten the core ethical principle of ‘do no harm’ and make it harder to account for and respond to the violence and mental health consequences of the USAID Stop Work Order.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"580-581"},"PeriodicalIF":8.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavioral, computational and self-reported measures of reward and punishment sensitivity as predictors of mental health characteristics","authors":"Stefano Vrizzi, Anis Najar, Cédric Lemogne, Stefano Palminteri, Mael Lebreton","doi":"10.1038/s44220-025-00427-1","DOIUrl":"10.1038/s44220-025-00427-1","url":null,"abstract":"Computational psychiatry proposes that behavioral task-derived computational measures can improve our understanding, diagnosis and treatment of neuropsychiatric disorders. However, recent meta-analyses in cognitive psychology suggest that behavioral and computational measures are less stable than self-reported surveys as assessed by test–retest correlations. If extended to mental health measures, this poses a challenge to the computational psychiatry agenda. To evaluate this challenge, we collected cross-sectional data from participants who performed a popular reinforcement-learning task twice (~5 months apart). Leveraging a well-validated neuro-computational framework, we compared the reliability of behavioral measures, computational parameters and psychological and mental health questionnaires. Despite the remarkable replicability of behavioral and computational measures averaged at the population level, their test–retest reliability at the individual level was surprisingly low. Furthermore, behavioral measures were essentially correlated only among themselves and generally unrelated to mental health symptoms. Overall, these findings challenge the translational potential of computational approaches for precision psychiatry. Reinforcement learning task-based behavioral and computational measures displayed low test–retest reliability at the individual level. Also in contrast to self-assessed personality measures, behavioral and computational measures were poor predictors of mental health measures, representing a challenge for computational psychiatry.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"654-666"},"PeriodicalIF":8.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dissecting causal relationships between cortical morphology and neuropsychiatric disorders: a bidirectional Mendelian randomization study","authors":"Bochao Danae Lin, Yunzhi Li, Anastasia A. Goula, Xiao Chang, Katrina L. Grasby, Sarah Medland, Ole A. Andreassen, Bart P. F. Rutten, Sinan Guloksuz, Dennis van der Meer, Jurjen J. Luykx","doi":"10.1038/s44220-025-00397-4","DOIUrl":"10.1038/s44220-025-00397-4","url":null,"abstract":"Brain cortical morphology, indexed by its surface area and thickness, is known to be highly heritable. Previous research has suggested a relationship of cortical morphology with several neuropsychiatric phenotypes. However, the multitude of potential confounders makes it difficult to establish causal relationships. Here we employ generalized summary-data-based Mendelian randomization and a series of sensitivity analyses to investigate causal links between 70 cortical morphology measures and 199 neuropsychiatric, behavioral and metabolic phenotypes. We show that total brain cortical surface area (TSA) has significant positive causal effects on 18 phenotypes. The strongest effects include TSA positively influencing cognitive performance, while reverse analyses reveal small effects of cognitive performance on TSA. Global mean cortical thickness (MTH) exhibits significant causal effects on five phenotypes, including schizophrenia. MTH reduces schizophrenia risk, and bidirectional causality is found between MTH and smoking initiation. Finally, in regional analyses, we detect positive influences of the transverse temporal surface area on cognitive performance and negative influences of transverse temporal thickness on schizophrenia risk. Overall, our results highlight bidirectional associations between TSA, MTH and neuropsychiatric traits. These insights offer potential avenues for intervention studies aimed at improving brain health. In this study, authors explore cortical morphology using Mendelian randomization to identify positive causal effects of total brain cortical surface on neuropsychiatric phenotypes.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"613-625"},"PeriodicalIF":8.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-sectional mega-analysis of resting-state alterations associated with autism and attention-deficit/hyperactivity disorder in children and adolescents","authors":"Luke J. Norman, Gustavo Sudre, Marine Bouyssi-Kobar, Megan Jiao, Stevi Gligorovic, Jenny Jean, Tonya White, Philip Shaw","doi":"10.1038/s44220-025-00431-5","DOIUrl":"10.1038/s44220-025-00431-5","url":null,"abstract":"Autism spectrum disorder (autism) and attention-deficit/hyperactivity disorder (ADHD) often co-occur, although it remains unclear whether these conditions share common neurobiological foundations or exhibit distinct alterations in resting-state brain connectivity. Here we conducted a cross-sectional mega-analytic comparison of functional connectivity patterns linked to autism and ADHD traits in children and adolescents (ages 6–19 years; n = 10,168), with follow-up analyses considering autism (n = 764 autistic; n = 893 neurotypical) and ADHD (n = 2,026 ADHD; n = 2,409 neurotypical) diagnoses. In total, 12,732 unique child and adolescent participants were included: 3,528 in both analyses, 6,640 in the trait analysis only, and 2,564 in the diagnostic analysis only. Autism traits and diagnosis were associated with reduced connectivity between the thalamus, putamen, salience/ventral attention and frontoparietal networks, whereas ADHD traits showed the opposite pattern. Hyperconnectivity between the default mode and dorsal attention networks was observed in both autistic and ADHD groups relative to neurotypical individuals and associated with ADHD traits. Despite frequent co-occurrence, autism and ADHD traits exhibit distinct neural signatures, with small effect sizes indicating subtle associations. A comparison of functional connectivity patterns linked to autism and attention-deficit/hyperactivity disorder traits in children and adolescents reveals distinct patterns of brain connectivity despite frequent co-occurrence of these conditions.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"709-723"},"PeriodicalIF":8.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic loneliness and isolation phenotypes, incident functional impairment and mortality in England between 2004 and 2018","authors":"Qian Gao, Andrew Steptoe, Daisy Fancourt","doi":"10.1038/s44220-025-00436-0","DOIUrl":"10.1038/s44220-025-00436-0","url":null,"abstract":"Social deficits are potential risk factors for premature mortality. Most research has focused on social deficits measured at single points in time. It remains unclear if the chronicity of loneliness affects its impact on adverse health outcomes. This study assessed the effects of chronic loneliness and isolation in predicting incident functional impairment and all-cause and cause-specific mortality. This longitudinal study used panel data from the English Longitudinal Study of Ageing, including 14 years of follow-up (waves 2–9, in 2004–2018). Social deficits over three waves (4 years) were measured using the UCLA loneliness scale and social isolation index, categorized as not present, fluctuating or chronic. We estimated the all-cause mortality risk with Cox proportional hazard modeling, and the Fine–Gray competing risk modeling was used to test the risk of functional impairment onset and cause-specific mortality. We analyzed 5,131 participants (mean age 67.6 years (s.d. 9.8)) in the mortality cohort (follow-up 9.8 years (IQR: 6.67–10.08)) and 4,279 participants (mean age 67.0 years (s.d. 9.6)) who were functional disability-free at baseline (follow-up 9.8 years (IQR: 7.17–10.17)). Compared with not being lonely/isolated, there was a higher risk of incident functional impairment among those with fluctuating loneliness (sub-hazard ratio (sHR) 1.30, 95% CI: 1.03–1.63) and chronic loneliness (sHR 1.58, 1.12–2.23), as well as chronic social isolation (sHR 1.41, 1.02–1.94). In survival analyses, compared with people who were not lonely/isolated, people experiencing fluctuating loneliness and social isolation had higher risks of all-cause mortality (loneliness HR 1.29, 1.13–1.48; isolation HR 1.15, 1.01–1.31). People with chronic isolation also had higher risks of all-cause mortality (HR 1.27, 1.05–1.55) and cancer-related mortality (sHR 1.69, 1.23–2.31). Over a 14 year follow-up, we found that chronic loneliness and isolation phenotypes were associated with aggravated risks of incident functional impairment and mortality. There was a potential dose–response relationship between chronicity of loneliness phenotypes and functional impairment onset and mortality. Preventing the onset of and transition to chronic loneliness and isolation in older age is a crucial target to support both the healthspan and the lifespan. Chronic loneliness and isolation significantly increase risks of functional impairment and mortality, as shown through longitudinal analysis of 5,131 participants using Cox proportional hazards and Fine–Gray models, highlighting the importance of addressing these social deficits in older adults.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"667-674"},"PeriodicalIF":8.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing anti-Asian discrimination in the USA through collaborative action","authors":"Stella S. Yi, Sze Wan ‘Celine’ Chan, Nelson Lin, Rachel Suss, Iyanrick John, Naheed Ahmed, Sugy Choi, Lan N. Đoàn, Simona C. Kwon","doi":"10.1038/s44220-025-00428-0","DOIUrl":"10.1038/s44220-025-00428-0","url":null,"abstract":"A comprehensive appraisal of community, academic and grassroots efforts to address anti-Asian discrimination reveals that public health research has focused on individual-level mental health impacts. Collectively, the research community should focus on asset-based approaches to prevent discrimination and address its mental, physical, and socioeconomic effects on Asian American communities.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"582-584"},"PeriodicalIF":8.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mental Health Month and the call to action","authors":"","doi":"10.1038/s44220-025-00441-3","DOIUrl":"10.1038/s44220-025-00441-3","url":null,"abstract":"May marks Mental Health Month in much of the world. The 2025 theme selected by Mental Health America is Turn Awareness into Action . Over the course of the month, campaigns also highlight awareness of mental health for specific groups, including children and older adults, as well as communities and support networks. The theme links the notion of harnessing increased awareness of mental health, including recognition of individual journeys and advances in mental health science, with action through advocacy and policy. This year’s Mental Health Month comes against a backdrop of a steadily increasing prevalence of mental health disorders, rising youth mental health concerns, increased suicide rates, and political and social upheaval — reinforcing the need for the mental health research community to come together to stave off the backslide of progress and to collectively work for sustainable solutions during this crisis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 5","pages":"479-480"},"PeriodicalIF":8.7,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s44220-025-00441-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of income and education reporting in psychedelic clinical trials","authors":"Daniel H. Grossman, Kevin R. Madden, Nicky J. Mehtani, Brian T. Anderson, Lori L. Davis, Jennifer M. Mitchell, Peter S. Hendricks","doi":"10.1038/s44220-025-00417-3","DOIUrl":"10.1038/s44220-025-00417-3","url":null,"abstract":"Socioeconomic status (SES) substantially influences mental health outcomes and treatment access, yet its reporting in psychedelic-assisted therapy trials remains underexplored. Here we systematically reviewed 98 articles (49 primary trials and 49 secondary analyses) published between 2006 and 2024 examining classic psychedelics and MDMA for mental health conditions. Only 12% of primary trials reported participant income data, and 31% reported educational attainment. In US-based trials, participants showed markedly higher SES than the general population: 93% had some college education (versus 62% nationally), and median incomes in major trials substantially exceeded the national median for all workers. Non-US trials showed variable patterns. This widespread underreporting of SES data and evidence of socioeconomic disparities, particularly in US trials, highlights an urgent need for standardized SES reporting and targeted strategies to improve socioeconomic diversity in psychedelic-assisted therapy research, ensuring broader generalizability and access to these emerging treatments. This systematic review analyzes how income and educational attainment are reported in psychedelic-assisted therapy trials. It reveals that only a minority of trials report this information and, when reported, the data have considerable variation in format and ambiguity in details. It also reveals disparities, with participants having higher income and being highly educated overrepresented in trials.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 5","pages":"567-574"},"PeriodicalIF":8.7,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hormonal contraceptive initiation after birth and the risk of depression","authors":"Ana Donnelly","doi":"10.1038/s44220-025-00437-z","DOIUrl":"10.1038/s44220-025-00437-z","url":null,"abstract":"","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 5","pages":"483-483"},"PeriodicalIF":8.7,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning to investigate policy-relevant social determinants of health and suicide rates in the United States","authors":"Yunyu Xiao, Yuan Meng, Timothy T. Brown, Alexander C. Tsai, Lonnie R. Snowden, Julian Chun-Chung Chow, Jyotishman Pathak, J. John Mann","doi":"10.1038/s44220-025-00424-4","DOIUrl":"10.1038/s44220-025-00424-4","url":null,"abstract":"This study aimed to categorize county clusters of multidimensional social determinants of health (SDOH) using unsupervised machine learning and to analyze their association with county-level suicide rates, considering temporal, geographic and demographic variation. We analyzed aggregated SDOH data across 3,018 US counties for 2009, 2014 and 2019, which were linked to county-level suicide rates from the National Vital Statistics System. We identified three distinct SDOH clusters: ‘REMOTE’ (rural, elderly, marginalized environments, old housing, traditional systems, empty houses), ‘COPE’ (complex family dynamics, high consumption of health services, poverty, extreme heat) and ‘DIVERSE’ (dense, immigrant rich, environmentally challenged, economically unequal, racial/ethnic diversity, saturated health care, expensive housing). We used negative binomial regression after identifying clusters to estimate the associations between county-level SDOH clusters and suicide rates. Compared with other clusters, REMOTE was associated with higher overall suicide rates, particularly among men; COPE showed elevated suicide rates among whites; and DIVERSE exhibited increased rates among women and Black and Hispanic populations. The distribution of suicide rates across US states corresponded to the variations in SDOH cluster distribution within each state. These findings provide a foundation for designing more effective, data-driven suicide prevention strategies tailored to specific regional and demographic contexts. This study addresses variations in suicide rates across the United States and the impact of three county-level clusters of social determinants of health characteristics. The authors used unsupervised machine learning to analyze data from 2009 to 2019, revealing that remote areas, characterized by rurality and older populations, had the highest suicide rates, highlighting the need for addressing disparities with targeted interventions.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 6","pages":"675-684"},"PeriodicalIF":8.7,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145123625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}